Investigation into the hierarchical nature of TQM variables using structural modelling

Article


Veltmeyer, Johan and Mohamed, Sherif. 2017. "Investigation into the hierarchical nature of TQM variables using structural modelling ." International Journal of Quality and Reliability Management. 34 (4), pp. 462-477. https://doi.org/10.1108/IJQRM-04-2015-0052
Article Title

Investigation into the hierarchical nature of TQM variables using structural modelling

ERA Journal ID19355
Article CategoryArticle
AuthorsVeltmeyer, Johan and Mohamed, Sherif
Journal TitleInternational Journal of Quality and Reliability Management
Journal Citation34 (4), pp. 462-477
Number of Pages16
Year2017
PublisherEmerald
Place of PublicationUnited Kingdom
ISSN0265-671X
1758-6682
Digital Object Identifier (DOI)https://doi.org/10.1108/IJQRM-04-2015-0052
Web Address (URL)https://www.emerald.com/insight/content/doi/10.1108/IJQRM-04-2015-0052/full/html
Abstract

Purpose
The purpose of this paper is to provide the empirical evidence supporting the existence of a multi-level hierarchical TQM model showing the structural inter-relationships among a total of 16 TQM variables (i.e. drivers, enablers and outcomes).

Design/methodology/approach
The set of identified TQM variables is the product of an in-depth review of the literature, and a robust reiterative process of verification and validation. Inter-relationships among the TQM variables were subjected to the scrutiny of a panel of experts, and were used as a basis for developing a web-based survey to explore the existence as well as strength of the structural relationship between each and every pair of the identified variables using interpretive structural modelling and MICMAC (Impact Matrix Cross-Reference Multiplication Applied to a Classification).

Findings
TQM variables were classified and clustered based on their influence and dependence on each other. Variables such as commitment by top management and customer satisfaction appear to have a strong chance to affect change, whereas variables such suppliers and competitors are very dependent on, and sensitive to, the evolution of the influent variables.

Originality/value
The paper demonstrates a multi-level TQM model encompassing all identified TQM drivers, enablers, and outcomes. The paper not only addresses a gap in the relevant literature (reduces the evidence scarcity about the hierarchical nature of TQM variables), but also gives insights into the variables with most driving power needing greater management attention.

KeywordsTotal quality management; Interpretive structural modelling; TQM drivers; Statistical process control; TQM enablers; Hierarchical nature
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400508. Infrastructure engineering and asset management
Public Notes

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Byline AffiliationsGriffith University
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